To learn from scribble annotations, the general method is using the partially Cross-Entropy (pCE) loss to train deep networks, where just labeled pixels are considered to calculate the gradient and the other pixels are ignored [1]}. However, due to the extremely sparse supervision, the pCE loss also can not achieve promising results. To solve this dilemma, Tang et al. [2]} proposed to integrate the pCE loss and MRF/CRF regularization terms to train deep networks with scribble annotations. After that, most of the recent weakly-supervised methods trained deep networks by using the following joint objective function [3]}, [4]}, [5]},
\(\mathcal {L}_{total} = \mathcal {L}_{pCE}+\lambda _{1}\mathcal {L}_{CRF}+\lambda _{2}\mathcal {L}_{other}\) 
